{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b2f1334",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec99cce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel('附件1：赛题A数据.xlsx','比赛数据-脱敏')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1f4f576",
   "metadata": {},
   "outputs": [],
   "source": [
    "area=[]\n",
    "for i in range(58):\n",
    "    area.append(data[data.小区编号==26019001+i])\n",
    "    area[i]=area[i].sort_values(by=['时间'])\n",
    "    area[i].to_csv('area2601900'+str(i+1)+'.csv',encoding='utf-8_sig',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "833245e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>小区内的平均用户数</th>\n",
       "      <th>小区内的最大用户数</th>\n",
       "      <th>平均激活用户数</th>\n",
       "      <th>最大激活用户数</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-08-28 00:00</td>\n",
       "      <td>5.1978</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0654</td>\n",
       "      <td>8</td>\n",
       "      <td>5235283136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-08-28 01:00</td>\n",
       "      <td>3.6233</td>\n",
       "      <td>11</td>\n",
       "      <td>0.8271</td>\n",
       "      <td>6</td>\n",
       "      <td>1142182848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-08-28 02:00</td>\n",
       "      <td>2.5051</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1883</td>\n",
       "      <td>4</td>\n",
       "      <td>242196960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-08-28 03:00</td>\n",
       "      <td>1.7267</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>3</td>\n",
       "      <td>86616496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-28 04:00</td>\n",
       "      <td>1.4299</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0468</td>\n",
       "      <td>3</td>\n",
       "      <td>449831120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-08-28 05:00</td>\n",
       "      <td>2.2406</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>3</td>\n",
       "      <td>1072054352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-08-28 06:00</td>\n",
       "      <td>3.4494</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>3</td>\n",
       "      <td>427489896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-08-28 07:00</td>\n",
       "      <td>3.7231</td>\n",
       "      <td>10</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>4</td>\n",
       "      <td>1268748168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-08-28 08:00</td>\n",
       "      <td>4.7513</td>\n",
       "      <td>12</td>\n",
       "      <td>0.4490</td>\n",
       "      <td>5</td>\n",
       "      <td>4135621016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-08-28 09:00</td>\n",
       "      <td>5.1125</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6620</td>\n",
       "      <td>6</td>\n",
       "      <td>10117650928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-08-28 10:00</td>\n",
       "      <td>4.7683</td>\n",
       "      <td>12</td>\n",
       "      <td>0.4662</td>\n",
       "      <td>5</td>\n",
       "      <td>2743870112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-08-28 11:00</td>\n",
       "      <td>7.6901</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0331</td>\n",
       "      <td>8</td>\n",
       "      <td>8099696168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-08-28 12:00</td>\n",
       "      <td>7.4515</td>\n",
       "      <td>15</td>\n",
       "      <td>1.3331</td>\n",
       "      <td>7</td>\n",
       "      <td>11830948448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-08-28 13:00</td>\n",
       "      <td>8.3061</td>\n",
       "      <td>17</td>\n",
       "      <td>0.9330</td>\n",
       "      <td>8</td>\n",
       "      <td>12487843216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-08-28 14:00</td>\n",
       "      <td>5.7111</td>\n",
       "      <td>15</td>\n",
       "      <td>0.6349</td>\n",
       "      <td>7</td>\n",
       "      <td>6947189112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-08-28 15:00</td>\n",
       "      <td>5.7576</td>\n",
       "      <td>15</td>\n",
       "      <td>0.8544</td>\n",
       "      <td>8</td>\n",
       "      <td>7902163560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-08-28 16:00</td>\n",
       "      <td>6.6777</td>\n",
       "      <td>15</td>\n",
       "      <td>0.8694</td>\n",
       "      <td>8</td>\n",
       "      <td>8371970144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-08-28 17:00</td>\n",
       "      <td>6.9744</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9603</td>\n",
       "      <td>7</td>\n",
       "      <td>14987029176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-08-28 18:00</td>\n",
       "      <td>6.3306</td>\n",
       "      <td>16</td>\n",
       "      <td>1.1075</td>\n",
       "      <td>8</td>\n",
       "      <td>12148813320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-08-28 19:00</td>\n",
       "      <td>7.6091</td>\n",
       "      <td>15</td>\n",
       "      <td>1.5199</td>\n",
       "      <td>8</td>\n",
       "      <td>13484443216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-08-28 20:00</td>\n",
       "      <td>7.5079</td>\n",
       "      <td>17</td>\n",
       "      <td>1.1301</td>\n",
       "      <td>8</td>\n",
       "      <td>8657559680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-08-28 21:00</td>\n",
       "      <td>7.5575</td>\n",
       "      <td>16</td>\n",
       "      <td>1.7232</td>\n",
       "      <td>10</td>\n",
       "      <td>13956481432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-08-28 22:00</td>\n",
       "      <td>7.3039</td>\n",
       "      <td>15</td>\n",
       "      <td>1.9119</td>\n",
       "      <td>8</td>\n",
       "      <td>5926743296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-08-28 23:00</td>\n",
       "      <td>7.8216</td>\n",
       "      <td>19</td>\n",
       "      <td>1.4462</td>\n",
       "      <td>8</td>\n",
       "      <td>11327029048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  时间  小区内的平均用户数  小区内的最大用户数  平均激活用户数  最大激活用户数            0\n",
       "0   2021-08-28 00:00     5.1978         13   1.0654        8   5235283136\n",
       "1   2021-08-28 01:00     3.6233         11   0.8271        6   1142182848\n",
       "2   2021-08-28 02:00     2.5051          9   0.1883        4    242196960\n",
       "3   2021-08-28 03:00     1.7267          8   0.0444        3     86616496\n",
       "4   2021-08-28 04:00     1.4299          7   0.0468        3    449831120\n",
       "5   2021-08-28 05:00     2.2406          7   0.0955        3   1072054352\n",
       "6   2021-08-28 06:00     3.4494          9   0.1320        3    427489896\n",
       "7   2021-08-28 07:00     3.7231         10   0.2196        4   1268748168\n",
       "8   2021-08-28 08:00     4.7513         12   0.4490        5   4135621016\n",
       "9   2021-08-28 09:00     5.1125         13   0.6620        6  10117650928\n",
       "10  2021-08-28 10:00     4.7683         12   0.4662        5   2743870112\n",
       "11  2021-08-28 11:00     7.6901         17   1.0331        8   8099696168\n",
       "12  2021-08-28 12:00     7.4515         15   1.3331        7  11830948448\n",
       "13  2021-08-28 13:00     8.3061         17   0.9330        8  12487843216\n",
       "14  2021-08-28 14:00     5.7111         15   0.6349        7   6947189112\n",
       "15  2021-08-28 15:00     5.7576         15   0.8544        8   7902163560\n",
       "16  2021-08-28 16:00     6.6777         15   0.8694        8   8371970144\n",
       "17  2021-08-28 17:00     6.9744         15   0.9603        7  14987029176\n",
       "18  2021-08-28 18:00     6.3306         16   1.1075        8  12148813320\n",
       "19  2021-08-28 19:00     7.6091         15   1.5199        8  13484443216\n",
       "20  2021-08-28 20:00     7.5079         17   1.1301        8   8657559680\n",
       "21  2021-08-28 21:00     7.5575         16   1.7232       10  13956481432\n",
       "22  2021-08-28 22:00     7.3039         15   1.9119        8   5926743296\n",
       "23  2021-08-28 23:00     7.8216         19   1.4462        8  11327029048"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('area26019001.csv')\n",
    "data=pd.concat([df.时间,\n",
    "                df.小区内的平均用户数,\n",
    "                df.小区内的最大用户数,\n",
    "                df.平均激活用户数,\n",
    "                df.最大激活用户数,\n",
    "                df.小区PDCP层所发送的下行数据的总吞吐量比特+\n",
    "                df.小区PDCP层所接收到的上行数据的总吞吐量比特\n",
    "               ]\n",
    "               ,axis=1)\n",
    "data.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6cbce7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=data.rename(columns={0:'总吞吐量'})\n",
    "data.小区内的平均用户数.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "45cf5c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差(MSE)：0.6793713032178743\n",
      "根均方误差(RMSE)：0.8242398335544542\n",
      "测试集R^2：0.9536715473122155\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import precision_score\n",
    "\n",
    "# 导入数据集，定义自变量、因变量\n",
    "dataset = data\n",
    "# dataset=dataset.drop()\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 2].values\n",
    "# print(X)\n",
    "\n",
    "# 对于分类字符串进行编码，LabelEncoder文本变数值，OneHotEncoder数值变OneHot编码\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "X[:,3] = labelencoder_X.fit_transform(X[:,3])\n",
    "onehotencoder = OneHotEncoder()\n",
    "X = onehotencoder.fit_transform(X).toarray()\n",
    "#避免虚拟变量陷阱\n",
    "X = X[:,1:]\n",
    "# 分离训练集与测试集 \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# 定义回归器，模型拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# 预测且查看pred与test数据\n",
    "y_pred = regressor.predict(X_test)\n",
    "np.set_printoptions(precision=2)#查看小数点精确度设定\n",
    "#np.concatenate数组拼接工具，首先转化为10*1维数组，进行纵向拼接查看\n",
    "# print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n",
    "\n",
    "# 查看相关矩阵，correlation matrix, 分析变量关系\n",
    "corrmat = dataset.corr()\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.rcParams['font.sans-serif']=['Arial Unicode MS']\n",
    "sns.set(font=\"Arial Unicode MS\")\n",
    "sns.heatmap(corrmat, vmax=.8, square=True)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#模型评估\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "print(f\"均方误差(MSE)：{mean_squared_error(y_pred, y_test)}\")\n",
    "print(f\"根均方误差(RMSE)：{np.sqrt(mean_squared_error(y_pred, y_test))}\")\n",
    "print(f\"测试集R^2：{r2_score(y_test, y_pred)}\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
